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"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import plotly.express as px # Plot graphics\n",
"import plotly.io as pio "
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"metadata": {},
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"# 2. Reading the data"
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"source": [
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"source": [
"## 2.1 First glances on the dataset"
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"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"xaxis": {
"anchor": "y",
"domain": [
0,
1
],
"title": {
"text": "writing score"
}
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"title": {
"text": "count"
}
}
}
},
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = px.histogram(df, x=\"writing score\")\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Correlation"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2022-06-29T00:54:49.552043Z",
"iopub.status.busy": "2022-06-29T00:54:49.551829Z",
"iopub.status.idle": "2022-06-29T00:54:49.630777Z",
"shell.execute_reply": "2022-06-29T00:54:49.629944Z",
"shell.execute_reply.started": "2022-06-29T00:54:49.552015Z"
}
},
"outputs": [
{
"data": {
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"config": {
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},
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}
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"\n",
"proportion['female'] = df[df['gender']=='female']['gender'].count()\n",
"proportion['male'] = df[df['gender']=='male']['gender'].count()\n",
"\n",
"print(proportion)"
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"fig = px.bar(x=labels, y=sizes)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 6. How's the proportion of parental level of education?"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
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"shell.execute_reply.started": "2022-06-29T00:54:49.745769Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"some college 0.226\n",
"associate's degree 0.222\n",
"high school 0.196\n",
"some high school 0.179\n",
"bachelor's degree 0.118\n",
"master's degree 0.059\n",
"Name: parental level of education, dtype: float64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['parental level of education'].value_counts('parental level of education')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 7. Which kind of lunch do the students prefer?"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2022-06-29T00:54:49.755591Z",
"iopub.status.busy": "2022-06-29T00:54:49.755234Z",
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},
"outputs": [
{
"data": {
"text/plain": [
"standard 0.645\n",
"free/reduced 0.355\n",
"Name: lunch, dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['lunch'].value_counts('lunch')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One can see that most students opt for standart lunch."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 8. Race"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2022-06-29T00:54:49.772490Z",
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"data": {
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"group C 0.319\n",
"group D 0.262\n",
"group B 0.190\n",
"group E 0.140\n",
"group A 0.089\n",
"Name: race/ethnicity, dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['race/ethnicity'].value_counts('race/ethnicity')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2022-06-29T00:54:49.788147Z",
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"name": "stdout",
"output_type": "stream",
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]
}
],
"source": [
"# Inserting the values above in a python dictionary.\n",
"race2 = {}\n",
"\n",
"groups = ['A', 'B', 'C', 'D', 'E']\n",
"\n",
"for i in groups:\n",
" \n",
" race2[f'group_{i}'] = df[df['race/ethnicity']==f'group {i}']['race/ethnicity'].count()\n",
"\n",
"print(race2)"
]
},
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}
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